1. Microsoft ® Site Server Commerce Edition Jay Sauls Microsoft Consulting Services

2. Overview Business Proposition Solution Architecture Shopper Experience Technology Solutions

3. Business Proposition Wider reach Reduced “friction” Always open High scalability Integration 24x7 availability Why go online? Online Requirements

4. Solution Architecture Microsoft Cluster Services used for failover capability on SQL Server Windows Load Balancing Service used for directing requests to a web server

5. Design SQL, Oracle Database Browser Wallet Windows NT Security, IIS, MTS Line of BusinessApplicationAnalyze Order Analysis Usage Import CommerceReportsEngage Ad Server Membership Personalization Authoring Tools TransactPipelines Order ProcessingOrder Processing Commerce InterchangeCommerce Interchange Third Party Components

6. Shopper Experience Browse SelectPurchase Product in stock? Special discount? Valid address? Valid credit card?

7. Pipeline Architecture Browse SelectPurchase Flag Inventory Inventory Sale Adjust Item Promo Item Price Shopper Info Product Info Product Pipeline Purchase Pipeline SendSMTP SaveReceipt Make PO Authorize CC Validate CC Validate bill_to SQLOrder

8. Pipeline Details } Transaction Order items Shopper info Payment Info Purchase Pipeline SendSMTP SaveReceipt Make PO Authorize CC Validate CC Validate bill_to SQLOrder

9. Data Mining Store owners need information How many visitors? How many buyers? When do they shop? What do they buy? Where are they from?

10. Data Mining Current Process Web Server logs imported into SQL Server one row at a time Each row is processed by a stored procedure Data from rows checked against dimension tables Only one import process can run at a time

11. Data Mining Leveraging MS Research Technology Realtime Recommendations Expands Intelligent Cross Sell from 3.0 Value-added functionality specific components Product cross-sell Personalized product recommendation Algorithms can be parameterized for confidence levels Segmentation - Find interesting sub-populations for targeting Label new user segments for advertising, promotions or direct mail Update User profiles Techniques Explicit via enabling Business User using OLAP tools with Data Warehouse Implicit via Clustering Algorithms from MS Research Clusters imply similarities in behavior or likely response

12. Real Time Events DTS Packages Custom Task Import Tasks User Usage Transaction CSF Catalog Interchange Win Media WhoIs Log Manager IP Resolution 33 Data Warehouse Architecture Event Data Transactions Interchange Apache IIS Other Events Win Media 11 Non-Event Data CSF Catalog User Content Other Data Site Vocab22 SQL Server OLE DB ADO 66 Data Warehouse Service Commerce OLE DB Provider ADO Cube MgrSchema Mgr CMD ProcROLAP Svc 55 Administration44

13. Data Mining Design Points Microsoft.com : > 100M hits / day = 30M – 40M “useful” hits Use OLAP cubes to view imported dimension data Number of dimension values is potentially unlimited Data will drive new Recommendations (Predictor) models

14. Data Mining Enhanced Design Rows are imported in batches of 3K – 4K per batch Batches are analyzed in-memory for distinct dimension values Distinct dimension values are added to SQL Server in batch update Multiple batches can run simultaneously Data can be partitioned across multiple databases

15. Targeting Applications Content Separation of page logic, format and data Allow a business user to easily manage, format and target content on a page without the need for a developer Allow developers Simple programming interfaces for the VID developer High performance and scalability (10ms/Slot) Extensible formatting templates Advertising Campaign or Campaign Item impression goals Exclusive targeting for sponsorships Exposure limits Support for Ad Networks (LinkExchange) Discount Campaign Management Reacts to product page and user’s basket Related Sells Campaign Management Supports Up-Sell, Cross-Sell and Inventory Sell Direct Mail Campaign Management Fast, Scalable, Runs as an NT Service Has a List Management object to support importing and merging of lists Campaign tracking of mails sent, clicked

16. Data Warehouse Offline Processing Predictor Engine Load Prediction Model Load Predictor Data Profile Definitions Expression Datastore Site Terms Datastore Expression Builder GUI Biz Desk App Design Time Store/ Retrieve Expressions Context Profile Schema User Profile Schema Biz Design Data Store Expr Evaluator User Profile Datastore Predictor Client Content Selection Framework Expr Evaluator ASP pages Run Time Context Profile User Profile Targeting Architecture

17. Content Selection Framework Business Desk Modules Content Dispatcher Content Advertising Content Mgmt Discounts Related Sells Campaign Mgmt Format Record Select Basket Score Product Score Size Filter Content Cache Mgr Format Record Select Score Load History NOD Size Filter Filter Format Record Select EvalTarget Content Selection Pipeline Advertising Selection Pipeline Discounts/Related Sell Pipeline User Exp Eval Exp Eval Context Catalog Content Selection Architecture

18. Recommendations Architecture IIS  scheduler launches model builder  ASP calls predictor to add recommendations  user goes to checkout page basket.asp  app downloads current model global.asa model data warehouse OLAP predictor service  IIS logs feedback basket data to the warehouse

19. Questions?

21. Predictor Component “Collaborative Filtering” Suggest products based on buying history Uses in-memory model Model allows identification of patterns Reduces overhead of manual cross-sells Developed in conjunction with MS Research

22. Predictor Component Disadvantages Each web server builds its’ own model in memory Model must be rebuilt on each server restart